Numerical Investigation of Heat Transfer and Fluid Flow Characteristics of Al2O3 Nanofluid In A Double Tube Heat Exchanger With Turbulator Insertion

One of the primary objectives associated with the double tube heat exchanger is to enhance the heat transfer rate and improve the overall system performance. A promising approach to achieve these improvements involves combining turbulator insertion and nanofluid techniques. This study presents a numerical investigation that examines the impact of Al2O3-water nanofluid within the inner tube, along with turbulator insertion in the form of square-shaped ribs, on the heat transfer and fluid flow characteristics of the double tube heat exchanger. The findings indicate that an increase in the volume fraction of nanofluid leads to an enhanced heat transfer rate. Additionally, reducing the spacing between the turbulator ribs in the double tube heat exchanger results in an increase of up to 50% in the Nusselt number compared to a heat exchanger without turbulator insertion. The maximum performance evaluation criterion achieved in this study is 1.05

Free convection heat transfer and entropy generation analysis of water-Fe 3 O 4 /CNT hybrid nanofluid in a concentric annulus

© 2018, Emerald Publishing Limited. Purpose: This paper aims to numerically investigate the heat transfer and entropy generation characteristics of water-based hybrid nanofluid in natural convection flow inside a concentric horizontal annulus. Design/methodology/approach: The hybrid nanofluid is prepared by suspending tetramethylammonium hydroxide-coated Fe 3 O 4 (magnetite) nanoparticles and gum arabic (GA)-coated carbon nanotubes (CNTs) in water. The effects of nanoparticle volume concentration and Rayleigh number on the streamlines, isotherms, average Nusselt number and the thermal, frictional and total entropy generation rates are investigated comprehensively. Findings: Results show the advantageous effect of hybrid nanofluid on the average Nusselt number. Furthermore, the study of entropy generation shows the increment of both frictional and thermal entropy generation rates by increasing Fe 3 O 4 and CNT concentrations at various Rayleigh numbers. Increasing Rayleigh number from 103 to 105, at Fe 3 O 4 concentration of 0.9 per cent and CNT concentration of 1.35 per cent, increases the average Nusselt number, thermal entropy generation rate and frictional entropy generation rate by 224.95, 224.65 and 155.25 per cent, respectively. Moreover, increasing the Fe 3 O 4 concentration from 0.5 to 0.9 per cent, at Rayleigh number of 105 and CNT concentration of 1.35 per cent, intensifies the average Nusselt number, thermal entropy generation rate and frictional entropy generation rate by 18.36, 22.78 and 72.7 per cent, respectively. Originality/value: To the best knowledge of the authors, there are not any archival publications considering the detailed behaviour of the natural convective heat transfer and entropy generation of hybrid nanofluid in a concentric annulus

Predicting parameters of heat transfer in a shell and tube heat exchanger using aluminum oxide nanofluid with artificial neural network (ANN) and self-organizing map (SOM)

This study is a model of artificial perceptron neural network including three inputs to predict the Nusselt number and energy consumption in the processing of tomato paste in a shelland-tube heat exchanger with aluminum oxide nanofluid. The Reynolds number in the range of 150–350, temperature in the range of 70–90 K, and nanoparticle concentration in the range of 2–4% were selected as network input variables, while the corresponding Nusselt number and energy consumption were considered as the network target. The network has 3 inputs, 1 hidden layer with 22 neurons and an output layer. The SOM neural network was also used to determine the number of winner neurons. The advanced optimal artificial neural network model shows a reasonable agreement in predicting experimental data with mean square errors of 0.0023357 and 0.00011465 and correlation coefficients of 0.9994 and 0.9993 for the Nusselt number and energy consumption data set. The obtained values of eMAX for the Nusselt number and energy consumption are 0.1114, and 0.02, respectively. Desirable results obtained for the two factors of correlation coefficient and mean square error indicate the successful prediction by artificial neural network with a topology of 3-22-2.https://www.mdpi.com/journal/sustainabilityMechanical and Aeronautical Engineerin

Numerical Investigation of the Thermo-Hydraulic Performance of Water-Based Nanofluids in a Dimpled Channel Flow using Al₂O₃, CuO, and Hybrid Al₂O₃-CuO as Nanoparticles

In this study, the authors study the impact of spherical dimple surfaces and nanofluid coolants on heat transfer and pressure drop. The main objective of this paper is to evaluate the thermal performance of nanofluids with respect to different Reynolds numbers (Re) and nanoparticle compositions in dimpled channel flow. Water-based nanofluids with Al2O3, CuO, and Al2O3-CuO nanoparticles are considered for this investigation with 1%, 2%, and 4% volume fraction for each nanofluid. The simulations are conducted at low Reynolds numbers varying from 500 to 1250, assuming constant and uniform heat flux. The effective properties of nanofluids are estimated using models proposed in the literature and are combined with the computational fluid dynamics solver ANSYS Fluent for the analysis. The results are discussed in terms of heat transfer coefficient, temperature distributions, pressure drop, Nusselt number, friction factors, and performance criterion for all the cases. For all cases of different nanoparticle compositions, the heat transfer coefficient was seen as 35%-46% higher for the dimpled channel in comparison with the smooth channel. Besides, it was observed that with increasing volume fraction, the values of heat transfer and pressure drop were increased. With a maximum of 25.18% increase in the thermal performance, the 1% Al2O3/water was found to be the best performing nanofluid at Re = 500 in the dimpled channel flow

Design of neuro-computing paradigms for nonlinear nanofluidic systems of MHD Jeffery–Hamel flow

© 2018 Taiwan Institute of Chemical Engineers In this paper, a neuro-heuristic technique by incorporating artificial neural network models (NNMs) optimized with sequential quadratic programming (SQP) is proposed to solve the dynamics of nanofluidics system based on magneto-hydrodynamic (MHD) Jeffery–Hamel (JHF) problem involving nano-meterials. Original partial differential equations associated with MHD–JHF are transformed into third order ordinary differential equations based model. Furthermore, the transformed system has been implemented by the differential equation NNMs (DE-NNMs) which are constructed by a defined error function using log-sigmoid, radial basis and tan-sigmoid windowing kernels. The parameters of DE-NNM of nanofluidics system are optimized with SQP algorithm. To illustrate the performance of the proposed system, MHD–JHF models with base-fluid water mixed with alumina, silver and copper nanoparticles for different Hartman numbers, Reynolds numbers, angles of the channel and volume fractions with three different proposed DE-NNMs are designed to evaluate. For comparison purpose, the proposed results with reference numerical solutions of Adams solver illustrate their worth. Statistical inferences through different performance indices are given to demostrate the accuracy, stability and robustness of the stochastic solvers